© 2014. Published by The Company of Biologists Ltd.
Regulation of Sec16 levels and dynamics links proliferation and
secretion
Kerstin D. Tillmann1,2, Veronika Reiterer1, Francesco Baschieri1,2, Julia Hoffmann3, Valentina
Millarte1,2, Mark A. Hauser1, Arnon Mazza4, Nir Atias4, Daniel F. Legler1, Roded Sharan4,
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Matthias Weiss3,5, Hesso Farhan1,2,5
1 Biotechnology Institute Thurgau (BITg) at the University of Konstanz, Unterseestrasse 47, CH8280 Kreuzlingen, Switzerland
2 University of Konstanz, Universitätsstrasse 10, Konstanz, Germany
3 Experimental Physics I, University of Bayreuth, Bayreuth Germany
4 Blavatnik School of Computer Science, Tel Aviv University, Tel-Aviv 69978, Israel
5 These authors contributed equally
Correspondence to:
Hesso Farhan
[email protected]
Matthias Weiss
[email protected]
Running title: ERES regulation by growth factor
Word count: 7971 (excluding references)
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JCS Advance Online Article. Posted on 19 December 2014
Abstract
We currently lack a broader mechanistic understanding of the integration of the early secretory
pathway with other homeostatic processes such as cell growth. Here, we explore the possibility
that Sec16A, a major constituent of endoplasmic reticulum exit sites (ERES), acts as an integrator
of growth factor signalling. Surprisingly, we find that Sec16A is a short-lived protein that is
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regulated by growth factors in a manner dependent on Egr family transcription factors. We
hypothesize that Sec16A acts as a central node in a coherent feed-forward loop that detects
persistent GF stimuli to increase ERES number. Consistent with this notion, Sec16A is also
regulated by short-term growth factor treatment that leads to increased turnover of Sec16A at
ERES. Finally, we demonstrate that Sec16A depletion reduces, while its overexpression increases
proliferation. Together with our finding that growth factors regulate Sec16A levels and its
dynamics on ERES, we propose this protein as an integrator linking growth factor signalling and
secretion. This provides a mechanistic basis for the previously proposed link between secretion
and proliferation.
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Introduction
Understanding cell homeostasis with respect to transcriptional, translational and posttranslational networks is a major challenge in cell and developmental biology. Intense research in
the past decades has fostered our understanding of a number of regulatory circuits such as those
that orchestrate cell polarity or cell proliferation. Conversely, in the field of membrane traffic, a
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comprehensive understanding of the integration of membrane traffic with other cellular processes
such a growth and proliferation is still fragmentary. In addition, little is known how external cues
like growth factors (GFs) regulate the functional organization of the early secretory pathway.
Export from the endoplasmic reticulum (ER) occurs on ribosome-free regions of the
rough ER called ER exit sites (ERES), where COPII vesicles form and shuttle nascent cargo
proteins to downstream sites of the secretory pathway (Budnik and Stephens,2009; Lee et
al.,2004; Zanetti et al.,2011). Sec16A (hereafter called Sec16) plays an important role in ERES
homeostasis as it interacts with several COPII components and regulates their function
(Bhattacharyya and Glick, 2007; Connerly et al.,2005; Farhan et al., 2008; Ivan et al.,2008;
Supek et al.,2002; Watson et al., 2006; Espenshade et al., 1995; Gimeno et al.,1996; Whittle and
Schwartz,2010; Kung et al.,2012). Various post-translational modifications such as
phosphorylation (Farhan et al., 2010; Koreishi et al.,2013; Lord et al.,2011; Sharpe et al.,2011),
ubiquitylation (Jin et al.,2012), or glycosylation (Dudognon et al.,2004) were reported to
modulate ER export, but the molecular details and functional consequences of this regulation
remain to be established. Several questions remain unanswered such as: how does
phosphorylation of Sec16 (Farhan et al, 2010) lead to changes in ERES? What dynamic and
molecular changes are associated with Sec16 phosphorylation? Is the biogenesis of ERES by GF
signaling solely contingent on Sec16 or does it involve COPII components? Answers to these
questions are important to obtain a full and integrative view of the regulation of the secretory
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pathway. Besides the regulation of the ER export machinery at the post-translational level, we
need to gain knowledge on whether and how regulation of protein abundance (for instance by
transcription factors) is relevant for the organization of the early secretory pathway.
Candidates for the latter are members of the early growth response (Egr) transcription
factor family that are known to mediate transcriptional responses downstream of several stimuli
that induce the Ras-MAPK signaling pathway. The Egr family, which comprises four members,
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exhibit a low basal expression level, but are rapidly (1-2 h) induced by MAPK signaling and bind
to a GCGG/TGGGCG consensus sequence in the promoter of target genes. Being early
responsive genes downstream of mitogenic signals, Egr family members are known to play a role
in controlling cell growth proliferation and differentiation (Sukhatme, 1990; Fowler et al, 2011).
In the current work, we show that growth factors modulate Sec16 protein levels and
dynamics. We propose that Sec16 acts as part of a coherent feed-forward loop, which integrates
secretion and growth factor signaling.
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Results
Sec16 integrates growth factor signaling at the level of ERES
In order to determine the role of Sec16 in integrating GF signaling, we first determined
whether the response of ERES to GF depletion requires Sec16. Therefore, we serum-starved cells
for 6 h which resulted in a robust reduction of ERES (labelled by Sec31 immunofluorescence).
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Importantly, knockdown of Sec16 inhibited this response, showing that the reduction of ERES
number by GF starvation causally depends on Sec16 (Fig.1A). However, this observation raises
the question whether any condition that reduces ERES number renders them un-responsive to
changes in GF levels. Therefore, we performed knockdown experiments with four kinases
(NME5, NME6, NME7, PIP5K1C), which we previously identified to reduce ERES number
(Farhan et al.,2010). Knockdown cells displayed on average ~25% reduction in ERES number
but did not affect the levels of Sec16 (Fig.S1A&B) indicating that the reduction of ERES number
occurs via a mechanism independent of Sec16 protein levels. In accordance with the
interpretation that GF signal to ERES via Sec16, none of these four knockdowns affected the
ability of ERES to respond to changes in GF status (Fig.S1A). We also performed a knockdown
of Sar1, the GTPase that initiates the COPII assembly cascade. Depletion of Sar1A reduces the
number of ERES and renders them insensitive to the amount of growth factors (Fig. S1),
similarly to what we observed in Sec16 depleted cells. Therefore, the ability of ERES to respond
to growth factors requires the presence of Sec16 and COPII.
Absence of growth factor signaling decreases Sec16 synthesis
To determine if absence of GFs alters ERES by changing the levels of Sec16, we serumstarved cells and observed a reduction of Sec16 levels with a halftime of about 2-3 h (Fig. 1B). In
contrast, Sec31 levels remained largely unchanged. Adding back growth factors (i.e. treatment
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with serum) increased Sec16 levels after approximately 3-4 hours of stimulation (Fig.1C). This
effect was dependent on de novo protein synthesis because it was absent in cells treated with
cycloheximide (Fig.1D). Thus, Sec16 levels are controlled by GF signaling.
The results shown above imply that Sec16 is a short-lived protein. Indeed, chasing Sec16
levels after blocking translation revealed that Sec16 has a half-life of approximately 2-3 h
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(Fig.2A). Serum starvation likewise resulted in a decrease of Sec16 levels (Fig.2B). The decrease
of Sec16 levels can be explained either by an increase of protein degradation, by a reduction in
the rate of synthesis, or by a combination of both. First, we determined whether Sec16 is in
principle degraded by the proteasome, which was the case since treatment with MG132 increased
Sec16 levels and prevented its decay in cycloheximide treated cells. If an increased degradation is
the main cause for the reduction of Sec16 levels under serum starvation, then we might expect
that the decay kinetics under serum-starvation are higher than under cycloheximide treatment.
This was not the case (Fig. 2B). Therefore, we conclude that, while degradation of Sec16 is in
principle mediated via the proteasome, the decay in Sec16 levels under serum-starvation are not
caused by an increase in the rate of proteasomal degradation. Thus, we next tested whether GF
signaling regulates Sec16 synthesis.
Control of Sec16 synthesis could in principle occur via storage of the mRNA in stress
granules, where it is trapped and prevented from translation. However, we did not observe any
evidence for the formation of stress granules as determined using staining for VCP, which labels
such structures (data not shown; Buchan et al, 2013). Another possibility is that growth factor
sensitive transcription factors control Sec16 levels. To test this, we bioinformatically analyzed a
500 bp region upstream of the transcription start site of the Sec16 gene. This region was scanned
for the presence of TF binding sites, which revealed over 90 candidates (supplementary table S1).
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These TFs were ranked based on the number of potential binding sites in the Sec16 promoter and
by the sum of their distance (in a protein-protein interaction network) to two growth factor
receptors, namely EGFR and IGFR. This approach revealed Egr family TFs as the most likely
candidates (Fig.3A). Individual knockdown of Egr1 or Egr3 did not reduce Sec16 expression (not
shown) but co-knockdown of Egr1 and Egr3 resulted in a clear reduction of Sec16 levels
(Fig.3B&C). Next, we determined whether the induction of Sec16 levels by mitogen treatment is
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dependent on Egr1 or Egr3. Treatment of cells with serum for 4 h resulted in a robust induction
of Sec16 levels, and this response was completely ablated in Egr1 or Egr3 knockdown cells
(Fig.3D). Of note, while single knockdowns of Egr1 or Egr3 did not per se reduce Sec16 levels,
they nevertheless prevented Sec16 induction upon serum treatment. Egr TFs are activated
downstream of ERK2 signaling, and accordingly, depletion of this kinase reduced Sec16 levels
(Fig.3E).
In accordance with their ability to regulate Sec16 levels, co-depletion of Egr1+3 resulted
in a reduction in the number of ERES (Fig.3F) and the extent of this reduction is comparable to
what we have previously observed in Sec16 knockdown cells (Fig.1, Farhan et al.,2010). We
used the recently described RUSH system (Boncompain et al,2012) to test for effects on ERGolgi trafficking. This system relies on ER-retention of secretory cargo using a streptavidinbased retention. Treatment of the cells with biotin relieves retention and liberates the cargo that
enters the secretory pathway. We used cells stably expressing GFP-tagged MannosidaseII
(ManII-RUSH). Knockdown of Egr1+3 led to a marked delay in the arrival of ManII-RUSH from
the ER to the Golgi (Fig.3G) and the effect was comparable to cells depleted of Sec16 (Fig.S2A).
No effect of Egr1+3 knockdown on Golgi to plasma membrane trafficking was observed (Fig.
S2B), thus excluding pleitropic effects on the secretory pathway.
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Cargo load has previously been shown to affect ERES number (Farhan et al, 2008; Guo et
al,2006) and therefore we determined whether the observed reduction of ERES in Egr1+3 cells is
due to a change in Sec16 levels or due to alterations in the synthesis of secretory proteins. To test
this, we used the hepatic cell line HepG2, which is a stronger secretory cell than HeLa cells.
Egr1+3 knockdown decreased the number of ERES (Fig.4). However, silencing Egr1+3 did not
affect the levels of alpha1 antitrypsin (AAT1) (Fig.4B), a major secretory protein in HepG2 cells
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(Nyfeler et al, 2008; Reiterer et al, 2010). A similar result was obtained with albumin, the most
prevalent cargo in hepatocytes (Fig. 4C). Therefore, it is unlikely that Egr1+3 knockdown
reduces secretory cargo load.
Sec16 as part of a coherent feed-forward loop (CFFL)
Our results so far revealed that GF signaling regulates Sec16 transcription, resulting in an
increase of Sec16 levels on a time-scale of 2-3 h after GF stimulation. In addition, GF signaling
pathways are expected to induce translation, thereby increasing secretory cargo load. This
scenario is reminiscent of a coherent feed-forward loop (Lim et al.,2013; Shen-Orr et al.,2002)
(CFFL; Fig.5A). In a CFFL, an input node (GFs) triggers a central node (Sec16) that
subsequently triggers an output node (secretion or ERES number). CFFLs are typically found as
part of persistence detectors, which ensure that transient stimuli that are able to trigger the central
node, do not affect the output node to any appreciable extent. Only a prolonged (i.e. a persistent)
stimulus is able to trigger the output node. A CFFL necessitates the presence of a fast connection
between input and central node (i.e. between GFs and Sec16). The connection ought to be
considerably faster than between input and output node (i.e. between GFs and secretion). We next
tested whether and how a brief GF treatment affects Sec16 and ERES organization.
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Growth factor treatment increases ERES number and alters Sec16 dynamics
We serum-starved cells, which results in a decrease in ERES number (Fig.1A) and briefly
(10-15 min) treated with fetal bovine serum (FBS). We observed a rapid increase of ERES
number, which was not the case in Sec16 depleted cells (Fig.5B). This rapid response was
dependent on ERK2 (Fig.S1D&E) and is unlikely to be mediated by induction of Sec16 levels,
because Sec16 levels did not change in the first 30 min of treatment (Fig.1).
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To gain more detailed insights into the mechanism that underlies the observed increase of
ERES number, we adapted a previously reported simulation approach for the self-assembly of
ERES (Heinzer et al.,2008). In agreement with experimental observations, our simulation
revealed a quasi-crystalline arrangement of ERES with a fairly uniform size and a comparatively
low density of Sec16 or COPII proteins on ER membranes between ERES. The steady state of
ERES formation therefore can be described by an unmixing scenario of the Flory-Huggins type
(Gedde,1995): Analogous to a domain formation of small amphipathic polymers in water, Sec16
and/or COPII show a dynamic segregation into patchy domains on ER membranes (=ERES).
However, in contrast to a standard Flory-Huggins scenario, Sec16 and/or COPII have finite
residence times on ER membranes, i.e. they dissociate on average with rate Γ from ER
membranes. Extending the Flory-Huggins scenario to include this aspect allowed us to
analytically predict the steady-state number and radius of ERES (NERES and RERES, respectively)
as a function of the protein density, φ, and the proteins’ dissociation rate and diffusion coefficient
(Γ and D, respectively): NERES= ΓL2/D and R ERES = 4φD/(πΓ) (L2 = area of the ER membrane).
The model predicts that increasing the dissociation constant of Sec16, Γ, increases the number of
Sec16-positive ERES irrespective of the available protein amount, φ. However, this happens at
the expense of ERES size since the ERES radius depends inversely on the dissociation rate Γ. In
other words, Sec16 molecules must be able to leave ERES faster on average to be able to
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nucleate new, but smaller ERES at remote locations (Fig.5C). This conjecture is supported by our
experimental finding that a brief (15 min) stimulation of serum-starved cells with FBS leads to a
reduction of the average size of ERES (Fig.5D). Live imaging of YFP-Sec31A showed that a
brief stimulation with FBS increased ERES number and reduced their size (Fig. 5E), which
agrees with the findings in fixed cells (Fig.5B&D).
To test the model, we performed fluorescence recovery after photobleaching (FRAP) of
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single ERES in HeLa cells expressing GFP-Sec16 (Fig.6A). We evaluated FRAP curves of
individual ERES by assuming a simple binding reaction, i.e. using a single-exponential recovery.
Sec16 association/dissociation events are stochastic in nature, i.e. a considerable variation of
fitting parameters between individual FRAP curves is anticipated. Therefore, averaging FRAP
curves to obtain a smoothed ‘master’ curve may introduce serious artifacts like stretchedexponential or even power-law recoveries (see, for example, Lee et al, 2001 for a related
discussion on the sum of exponentials). We therefore fitted curves individually and averaged the
obtained fitting parameters. Curves from starved and mitogen-treated cells did not vary with
respect to half-time of recovery (~10s). However, the extent of the recovery varied: As compared
to their pre-bleach fluorescence (set to unity), the average extent of fluorescence recovery of
ERES was larger and the mobile fraction higher when cells had been stimulated with GFs (Fig.
6B&G), which is in line with our previous observations (Farhan et al, 2010).
If only a single pool of Sec16-GFP was present on ERES, one would expect either a full
recovery (q=1) or no recovery at all (q =0) on the time scale of the FRAP experiment. Observing
a recovery with a maximum value of q between zero and unity therefore requires the existence of
a fast Sec16 pool that carries the observed recovery (relative amount q), and a slow Sec16 pool
that does not contribute to the fluorescence recovery on the time scale of the FRAP experiment
(relative amount 1-q). Consequently, the arithmetically averaged dissociation rate of the entire
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Sec16 population is Γ=qΓfast+(1-q)Γslow. Observing an increase in q after GF stimulation therefore
reports on a faster turnover kinetics of Sec16 on average (Γ is increased), while the half time of
the recovery is only determined by Γfast (since the experiment takes much less time than 1/Γslow).
Yet, an increase in the average dissociation rate Γ is exactly the prediction of the above model,
and we therefore conclude that mobilization of Sec16 after mitogen stimulation is a factor that
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can drive formation of new ERES.
Number and size of ERES also depend on the diffusion constant of Sec16 on ER
membranes, D. Thus, if our above rationale is to hold true, D must vary to a lesser extent than
Γ between starved and mitogen-treated cells. To probe this, we used fluorescence correlation
spectroscopy (FCS) that allowed us to determine the diffusion constants of GFP-Sec16 in
cytoplasm and on ER membranes (representative FCS curves in Fig.S3). We found that the
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diffusion constant of GFP-Sec16 in cytoplasm agreed well with theoretical predictions for a
soluble protein of ~280 kDa, irrespective of the treatment (D≈16μm2/s). In contrast, the
membrane-bound pool of Sec16 showed a reduction in the diffusion constant under serumstarvation (D≈0.43μm2/s) as compared to mitogen-treated cells (D≈0.65μm2/s) and completely
untreated cells (D≈0.62μm2/s). This increase in diffusion upon mitogen treatment was in the
range of ~50% and is thus smaller than the increase in the mobile fraction, which doubled after
mitogen treatment (Fig. 6G). Most likely, the change in D and in Γ are linked: Γ is the weighted
average of two dissociation constants, is Γ=qΓfast+(1-q)Γslow, i.e. increasing Γ most likely means
that the fast dissociation gains more weight (q increases). Given that Γslow is associated with a
pool of Sec16 that is stuck in ERES (immobile on the time scale of our experiments), this
“mobilization” increases the amount of Sec16 molecules on ER regions between ERES.
Increasing the pool of these free Sec16 molecules possibly leads to a more pronounced
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interaction with other ER-resident components, e.g. resulting in a transient formation of
oligomeric structures, which consequently reduces the average diffusion of Sec16 copies on ER
patches between ERES. Nevertheless, Sec16 is regulated on the time scale of minutes consistent
with being part of a CFFL. We next aimed at understanding how mobilization of Sec16 generates
more ERES.
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Interaction with COPII modulates the turnover of Sec16 on ERES
We hypothesized that an interaction with COPII is responsible for the observed existence
of a fast and a slow Sec16 pool and that thereby COPII plays a role in the ability of Sec16 to
generate new ERES. If true, then decreasing the binding of COPII to ERES ought to affect the
amount of fluorescence recovery, q, in FRAP experiments. As a first step, we treated cells with
cycloheximide to reduce the protein load in the ER and concomitant the association of COPII
with ERES (Farhan et al.,2008; Forster et al.,2006). While GF treatment in control cells led to an
increase in Sec16 fluorescence recovery, the extent of recovery and the mobile fraction in
cycloheximide treated cells was the same for starved and mitogen-treated cells (Fig.6C&G). To
obtain further insights into the role of COPII, we performed FRAP experiments with a truncation
mutant of Sec16 that lacks the last C-terminal 431 amino acids, which we called Sec16-Maddin.
This mutant still contains the previously described ERK phosphorylation site Thr415 (Farhan et
al,2010) but it lacks the domain that was described to bind Sec23A and Sec12 (Bhattacharyya
and Glick,2007; Montegna et al.,2012). Using co-immunoprecipitation we confirmed that Sec16Maddin fails to interact with Sec23A, but is still able to interact with Sec31 (Fig.6H) as the
Sec31-binding region has been previously mapped to the central region of Sec16 (Shaywitz et
al,1997). In FRAP experiments, treatment with GFs did not increase but rather decreased the
extent of recovery and mobile fraction of Sec16-Maddin (Fig.6D&G). To further support the
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notion on the role of COPII, we depleted Sar1A and found EGF treatment did not change GFPSec16 mobility (Fig.6E&G). Of note, depletion of Sar1A also affected the ability to form new
ERES upon GF treatment (Figs.1A&5B). Depletion of NME6 lowered the number of ERES, but
did not affect the ability of ERES to respond to growth factor treatment (Fig.S1A) and also did
not inhibit the increase in GFP-Sec16 mobility after EGF treatment (Fig.6F&G). Thus, we
propose that the COPII-Sec16 interplay might be important for generation of new ERES after
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growth factor treatment.
Interaction with COPII is required for Sec16 to generate more ERES
To test the role of the Sec16-COPII interplay in the generation of more ERES after
mitogen treatment, we determined the ERES number in cells expressing either wild type GFPSec16 or Sec23-binding deficient GFP-Sec16-Maddin. We only compared cells within a twofold
range of fluorescence intensities to avoid inclusion of cells with a too high variability of GFPSec16 overexpression. Cells were fixed before and after treatment with serum for 15 minutes
followed by confocal imaging. If the ability of Sec16 to interact with COPII is critical to generate
more ERES, then Sec16-Maddin should fail to respond to GF treatment. As observed with
endogenous Sec16, GF treatment increased the number of ERES labeled with wild-type GFPSec16 (Fig.7A). As a control, we tested whether the response requires Sec16 phosphorylation of
Thr415 and found this to be the case (Fig.5A). ERES number also did not increase after mitogen
stimulation in cells with GFP-Sec16-Maddin (Fig.7A). Hence, the Sec16-Sec23 seems to be
relevant for the increase in the number of ERES upon mitogen treatment.
Finally, we tested the role of COPII using an in vitro recruitment assay. Semi-intact cells
were extensively washed to strip off the majority of Sec16 and COPII from their endomembranes
(Fig.7B), followed by incubation with fresh cytosol, which led to recruitment of COPII and
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Sec16 and formation of new ERES. Reducing the load of secretory cargo by treatment with
cycloheximide significantly reduced de novo formation of Sec16- and COPII-positive ERES
(Fig.7B-D). Altogether, our data underscore the necessity for Sec16 mobilization for ERES
generation, and highlight that cooperation of Sec16 with COPII is required for formation of new
ERES after mitogen treatment.
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Cell proliferation is dependent on Sec16
Our results so far indicate that Sec16 integrates mitogenic signalling at the level of ERES.
Yet it is so far unclear whether Sec16 is relevant for the biologic outcome of mitogenic
signalling, such a proliferation. In our previous work we identified 64 kinases and phosphatases
that, when depleted, reduce the number of ERES (Farhan et al.,2010). More recently, others
described 47 proteins that regulate ERES number (Simpson et al.,2012). We hypothesized that, if
ER export is linked to the regulation of cell proliferation, then a network linking these hits (111)
to Sec16 ought to be enriched in cellular processes related to cell proliferation. We therefore
constructed an anchored network of these 111 hits with Sec16 as an anchor, using a previously
described algorithm (Yosef et al.,2011). This anchored network was analysed for enrichment of
biological processes (GO term annotation). Indeed, this approach revealed several proliferationrelated processes that were significantly enriched (Fig. 8A), suggesting a link between ER export
and cell proliferation. To test this experimentally, we performed a Sec16 knockdown and
measured the increase in cell number for two days. We found that depletion of Sec16 inhibited
cell proliferation (Fig.8B), and this effect was not attributed to an increase apoptosis as assessed
by determining caspase-3 cleavage (Fig.8C). The Ras-ERK1/2 pathway is well known regulator
of proliferation and it is also known to induce Egr family members, which we experimentally
verify (Fig.8D). In line with our finding that Egr members control Sec16 levels, Ras
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overexpression also induced a 3-fold increase in Sec16 (Fig. 8D). We tested whether Sec16 is
involved in the Egr-dependent control of proliferation. Silencing Egr1+3 resulted in an inhibition
of cell proliferation, which was overcome by overexpressing Sec16 (Fig.8E). Overexpressing
Sec16 significantly induced proliferation (Fig. 8E and FigS4), indicating that Sec16 is necessary
and sufficient for proliferation. We also overexpressed the Sec16-T415I mutant as well as Sec16Maddin and determined the effect on proliferation. HeLa cells overexpressing these mutants
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proliferated stronger than GFP-expressing cells, but significantly less than cells expressing wild
type Sec16. Sec16 forms oligomers and Sec16 mutants are expected to oligomerize with
endogenous wild type Sec16. We propose that this is the reason why these mutants themselves
induce proliferation, although significantly less than overexpressing wild type Sec16. We could
not test the overexpression of Sec16 variants in cells depleted of endogenous Sec16 because
Sec16 silenced cells did not tolerate the overexpression of any plasmid. To test whether any
regulator of ER export would behave similarly, we depleted Sar1A and found that it reduces
proliferation (Fig. 8B). However, overexpression of Sar1A had no detectable effect on
proliferation (Fig. 8E).
Altogether, the notion that ER export and cell proliferation are linked is supported by our
findings and we propose Sec16 as the molecular integrator of these two processes.
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Discussion
The integration of metabolic and signaling networks into the secretory pathway remains
poorly understood. In the current work, we demonstrated how Sec16 acts as an integrator of GF
signaling into ERES. We propose Sec16 to be part of a CFFL that functions as persistence
detector for increasing secretory flux upon mitogen signaling: Transient GF stimulation increase
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ERES number, but does not affect the output (i.e. secretion) on a short timescale. This rapid
increase in ERES number is predicted by our modeling to happen at the expense of ERES size:
While ERES are rapidly increasing to be able to cope with an upcoming wave of secretory cargo,
they only acquire an appropriate size on larger time scales. Concomitant to an increase in cargo
load, Sec16 levels are induced (in a manner dependent on Egr1+3) to compensate for the reduced
size of ERES, rendering ERES fully capable to deal with a higher secretory flux after a few
hours. How is Sec16 regulated by EGR1&3? We did not perform a promoter dissection to test
whether these transcription factors bind the Sec16 promoter. Sec16 expression might also be
regulated via mechanisms other than direct promoter control. For instance Sec16 mRNA could be
stored and released upon changes in growth factor levels. However, we did not observe evidence
for storage granule formation under serum starvation. Another possibility is that Sec16 could be
regulated by miRNAs. For instance, miR-212 and miR-132 were shown to be regulated by Egr
family TFs (Wang et al, 2010) and according to our database search we found that Sec16 is
regulated by these miRNAs (www.genecards.org).
Sec16 was shown to bind to ERES through an arginine-rich region without major help
from co-factors (Ivan et al.,2008). Others showed that Sec16 remains associated with ERES
throughout mitosis without the presence of COPII (Hughes et al.,2009). Therefore, it was
proposed that Sec16 is able to cluster at domains of the ER and to form a template for the
biogenesis of ERES that acts independently of COPII components. Yet, it remains unclear how
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this “template” function is mediated and how and if it is regulated dynamically. While this
“template” model may be correct in the context of mitotic dis- and re-assembly of ERES, we
propose that the situation is more dynamic in the context of an adaptive response of ERES to GF
signaling. The fact that Sec16-Maddin that is unable to bind Sec23 did not respond to GF
treatment indicates that the adaptive response to GFs (i.e. the rapid increase of ERES number)
requires an interaction of Sec16 with COPII. Once recruited to the ERES, Sec16 would then act
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to stabilize COPII on ERES for instance reducing the rate of GTP hydrolysis of Sar1 (Kung et
al.,2012). Unlike other, more static interpretations, our model of an adjustable mutual
stabilization of Sec16 and COPII components is capable of including a dynamic cellular response
while remaining fully compatible with key features of the cell’s steady state, e.g. a strict COPIIdependent emergence of a secretory flux. While this study focused the role of mitogens in
regulating ERES, others have recently identified in Drosophila a novel response of ERES to
amino-acid starvation (Zacharogianni et al, 2014), which is distinct from the response to serumstarvation reported earlier (Zacharogianni et al, 2011). Amino-acid starvation resulted in
formation of so-called Sec bodies where Sec16 and other COPII components were stored. These
structures formed in a manner dependent on Sec24AB and Sec16, illustrating a cooperation
between Sec16 and COPII components in the response to stress. This is analogous to our findings
that also show that the binary interaction of Sec16 with COPII is essential for ERES to properly
respond to alterations in growth factor levels.
Our findings also indicate that Sec16 (or secretion in general) is relevant for the ability of
cells to proliferate. Based on this, we are tempted to speculate that Sec16 expression is linked to
conditions of de-regulated cellular proliferation such as cancer. Indeed, such a conclusion is
facilitated by a recent screen of the proteome of 8 colonic cancer patients comparing healthy
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colonic tissue with the diseased parts: Sec16 was upregulated approximately twofold in colonic
cancer samples compared to healthy tissue (Wisniewski et al.,2012). This was not due to general
up-regulation of all secretory pathway components as ERGIC-53 was downregulated
(Wisniewski et al., 2012) which is in agreement with earlier reports on the link between ERGIC53 and cancer (Roeckel et al., 2009). Our findings demonstrate that changes in the number of
ERES are part of the cellular response to mitogen treatment and therefore imply a close link
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between the regulation of secretion and cell growth and proliferation. An integrative view on the
secretory pathway becomes even more important when aiming at de-convolving the results of
recent systems biology approaches that have identified numerous signaling, metabolic or
transcriptional regulators which have an effect on the secretory pathway at various levels (Bard et
al., 2006; Farhan et al.,2010; Kondylis et al.,2011; Simpson et al.,2012; Chia et al, 2012).
Conflict of Interest
The authors declare that there is no conflict of interest.
18
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Materials and Methods
Fluorescence recovery after photobleaching (FRAP)
FRAP was performed with a LeicaSP5 confocal microscope using a 63x/1.4NA oil immersion
objective at 5 fold digital magnification. All experiments were performed at 37°C. Glass cover
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slips were transferred to a Ludin chamber (Life Imaging Services GmbH) and covered with
imaging medium (DMEM supplemented with 20 mM Hepes, pH 7.4). After acquisition of a prebleach image, the ERES was bleached at 100% laser intensity for 750 ms. After bleaching,
images were acquired at one image per second. Images were analyzed using ImageJ. For fitting
FRAP curves, we used the reaction-dominated single-exponential recovery that has been widely
used in the literature (Sprague et al, 2006). The mobile fraction was calculated via q=(F∞ −
F0)/(Fi − F0), where F∞ is fluorescence in the bleached region after recovery, Fi is the
fluorescence in the bleached region before bleaching, and F0 is the fluorescence in the bleached
region directly after bleaching.
ERES quantification
ERES were quantified in cells immunostained for Sec16 or Sec31 or in cells expressing GFPSec16. Images were acquired using a LeicaSP5 confocal microscope, with a 63x/1.4NA oil
immersion objective at 3 fold digital magnification. ERES were quantified using ImageJ by
applying uniform thresholding to the images to exclude non-specific structures. Structures
smaller than 2 pixels in size were excluded from analysis as these typically represented noise
originating from image pixelization. In the case of GFP-Sec16 expressing cells, total fluorescence
intensity of cells was measured, and ERES were only quantified from cells displaying
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comparable fluorescence intensity (within twofold intensity range), thereby excluding possible
effects due to the grade of GFP-Sec16 overexpression.
Regulatory sequence analysis
We used the PRIMA algorithm (Elkon et al.,2003) to scan the 500bp region upstream to the
transcription start site for enriched binding sites. Enrichment was calculated with respect to
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similar 500bp regions upstream to the transcription start site for the entire genome. This analysis
revealed 184 enriched matrices corresponding to 180 TFs. We further scored each enriched TF
according to its shortest distance in a protein-protein interaction network from either EGFR or
IGF1R and ranked the TFs accordingly. Sequences and annotations for the regions, based on
Human Genome build 19, were downloaded from EMBL on 30/8/12. Transcription factor
binding sites, represented as position weigh matrices were downloaded from TRANSFAC (Matys
et al.,2006) database release 11.1. A protein-protein interaction network was taken from ANAT
(Yosef et al.,2011).
Fluorescence Correlation Spectroscopy
FCS measurements were performed on a Leica SP5 SMD system equipped with a custom-made
climate chamber for 37°C incubation. Samples were illuminated at 488nm via a water immersion
objective (HCX PL APO 63x1.2W CORR), fluorescence detection used a bandpass filter (500530nm); pinhole was set to one Airy unit. FCS data were fitted using the fitting function for two
non-interacting populations with normal diffusion:
C( τ) =
fA
(1 − f ) A
+
(1)
(1 + τ/τD ) (1 + τ/τ(2)D )
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2
The first term with amplitude f and diffusion time τ(1)
describes a fast diffusing
D = r0 /(4D c )
species, e.g. cytosolic Sec16-GFP, while the second term with amplitude (1− f ) and diffusion
time τ(D2) = r02 /(4D m ) describes a slow diffusing species, e.g. membrane-bound Sec16-GFP. Both
diffusion times are determined by the radius of the confocal volume, r0 ≈ 220nm, and the
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respective diffusion constants, Dc and Dm. For simplicity, we have neglected a factor
(1+ τ/(S τ ))
2 ( 2)
D
in the denominator of the first summand. This factor captures diffusion along
the optical axis, yet due to the unavoidable elongation of the confocal volume (described by
S2≈25) it had little influence on the fit parameters reported here. The prefactor A is proportional
to the inverse number of GFP-tagged Sec16 molecules in the focus (here: typically 20-100) and it
also encodes GFP’s photophysics on time scales of ~10μs. Since all diffusion times were well
beyond 300μs, the photophysics’ contribution to A was negligible for the fitting process.
Autocorrelation curves were collected for 60 seconds in regions of the peripheral ER, i.e. away
from the nuclear rim, for several loci in a variety of cells and treatments. FCS curves were fitted
individually, and the mean of the obtained diffusion coefficients (24 curves for untreated cells; 16
for starved and mitogen-treated cells) are reported in the main text.
Theoretical predictions for diffusion constants of Sec16 in cytosol and on membranes
were derived as follows. We assumed Sec16 to be globular with a mass about 10fold larger than
GFP and a hydrodynamic radius ~2.2fold larger than GFP, i.e. R=3.3nm. Based on the EinsteinStokes equation D = k BT /(6πηR) the diffusion constant is Dc=17μm2/s. We used a cytosolic
viscosity η 4fold larger than that of water (Elsner et al.,2003); kBT is thermal energy. For Sec16’s
diffusion on membranes, we employed the Saffman-Delbruck relation for peripheral membrane
24
proteins (Morozova et al.,2011), from which one infers a diffusion constant of Sec16 in the gross
range of Dm=1μm2/s.
Acknowledgements
HF is supported by a grant from the Swiss National Science Foundation (SNF), by a R’Equipe
Science Foundation and by the Canton of Thurgau. JH and MW acknowledge support by DIP
grant GA 309/10-1, DFG grant WE4335/2-1, and HFSP grant RGY0076/2010. MW would like
to thank H. Brandt and W. Zimmermann for helpful discussions on phase separation scenarios.
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grant from the SNF, by the Young Scholar Fund of the University of Konstanz, by the German
25
Figure legends
Figure 1. GF levels regulate Sec16 expression levels. A, HeLa cells were transfected with control
siRNA (control) or siRNA to Sec16 (siSec16). After 72 h, cells were left in steady state (SS), or
were serum starved for 6 h (Strv) followed by fixation, Sec31 immunostaining, and confocal
microscopy. Right panel shows an immunoblot demonstrating efficiency of Sec16 knockdown
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and a graphic representation of the number of ERES per cell presented as percent of control in
steady state. This value amounts to 156.4 ± 32 ERES. Results are means ± SD from three
independent experiments with more than 50 cells per experiment. B, HeLa cells were either left
untreated (NT) or serum-starved for the indicated time points. Cell lysates were immunoblotted
against the indicated proteins. Left panel shows a representative experiment and the right panel
shows an evaluation of three independent experiments of Sec16 (black bars) and Sec31 (grey
bars) levels. Values are ±SD and are represented as percent of NT. C, HeLa cells were serumstarved for 4 h (Strv) or treated with 10% FBS as indicated. Cell lysates were immunoblotted
against the indicated proteins. The left panel shows a representative experiment and the right
panel shows an evaluation of three independent experiments depicting expression of Sec16 (black
bars) and Sec31 (grey bars). Values are ±SD and presented as % of the serum-starved condition.
D, HeLa cells were either left untreated (NT), or stimulated with 10% FBS for 4h. CHX indicates
cycloheximide treatment for 30 min prior to FBS stimulation. Cell lysates were immunoblotted
against the indicated proteins. Scale bars in this image are 10µm.
Figure 2. Sec16 is a short-lived protein which is rescued by proteasomal inhibition. A, HeLa
cells were either left untreated (NT), or treated with cycloheximide (CHX) as indicated.
Alternatively cells were pre-treated with MG132 for 30 min prior to cycloheximide addition
26
(+MG132). Cells were lysed and immunoblotted against the indicated proteins. The upper panel
shows a representative experiment and the lower panel shows an evaluation of three independent
experiments. Values are ±SD and presented as percent of NT. B, HeLa cells were either left
untreated (NT), or serum-starved for the indicated time points. Alternatively cells were pretreated with MG132 for 30 min prior to serum-starvation (+MG132). Cells were lysed and lysates
were subjected to SDS-PAGE and immunoblotting against the indicated proteins. The upper
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panel shows a representative experiment and the lower panel shows an evaluation of three
independent experiments depicting expression of Sec16. Values are ±SD and are represented as
percent of NT.
Figure 3. Egr family transcription factors regulate Sec16 expression. A, Schematic
representation of the result of our bioinformatic analysis as described in the text. Green nodes are
TFs predicted to bind to the Sec16 promoter. Red nodes are the two growth factor receptors to
which the transcription factors were linked to via intermediate proteins (purple nodes). Edges are
experimentally documented physical interactions. B&C, HeLa cells were transfected with nontargeting siRNA (control) or with two different combinations of siRNA against Egr1 and Egr3.
After 72 h, cells were lysed and immunoblotted against the indicated proteins. D, HeLa cells
were transfected with non-targeting siRNA (control) or with siRNA against Egr1 or Egr3. After
72 h, cells were either harvested directly (NT) or treated with 10% FBS (FBS) for 4 h prior to
lysis and immunoblotting as indicated. HeLa cells were transfected with non-targeting siRNA
(control) or with siRNA against ERK2 (ERK2-KD). E, Cells were lysed after 72 h and
immunoblotted against the indicated proteins. F, HeLa cells were transfected with control siRNA
(control) or siRNA to Egr1 and Egr3 (Egr1+3). After 72 h, cells were fixed, stained for Sec31
and imaged using confocal microscopy. ERES were counted using ImageJ. Results are presented
27
as percent of control and this value amounts to 244.25 ± 13.62 ERES. Results are means ± SD
from three independent experiments where at least 50 cells were evaluated per experiment.
Asterisks indicate statistically significant differences (*, P < 0.05) G, HeLa cells stably
expressing GFP-tagged MannosidaseII RUSH-construct (ManII-RUSH) were transfected with
control siRNA (control) or siRNA to Egr1 and Egr3 (Egr1+3). After 72 h, ManII-GFP was
released by adding Biotin and cells were fixed at the indicated subsequent time points. Lower
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panel shows a bar graph of fluorescence intensity at Golgi area, normalized to ER fluorescence,
presented as fold increase over t0. This value amounts to 9.52 ± 3.6 in control cells and 9.27 ± 3
or 12.32 ± 2.6 in clones #1 and #2 in Egr1+3 knockdown cells, respectively. Results are means ±
SD from three independent experiments with at least 50 cells per experiment. Asterisks indicate
statistically significant differences (*, P < 0.05) as determined by ANOVA with Tukey’s post hoc
test. Scale bars in this image are 10µm.
Figure 4. Decrease of ERES number after loss of Egr1+3 is independent of cargo load in HepG2
cells. A, HepG2 cells were transfected with control siRNA (control) or siRNA to Egr1 and Egr3
(Egr1+3). After 72 h, cells were fixed and stained for Sec31, and images were acquired by
confocal microscopy. Left panel shows graphic representation of the number of ERES per cell.
Results are presented as percent of control to account for inter-assay variance. This value
amounts to 314 ± 51 ERES. The results are means ± SD from three independent experiments
where at least 50 cells were evaluated per experiment. Asterisks indicate statistically significant
differences (*, P < 0.05) as determined by ANOVA with Tukey’s post hoc test. B&C, HepG2
cells were transfected with non-targeting siRNA (control) or with siRNA against Egr1 and Egr3.
After 72 h, cells were lysed and immunoblotted against a1-antitrypsin (AAT) in panel B or
against Albumin in panel C. The left parts show representative experiments and the right parts
28
show evaluation of three independent experiments. Values are ±SD and are represented as
percent of control. Scale bars in this image are 10µm.
Figure 5. Fast response of Sec16 to growth factors. A, Schematic representation of the proposed
coherent feed-forward loop. B, HeLa cells were transfected with control siRNA (siControl) or
siRNA to Sec16 (siSec16). After 72 h, cells were serum starved for 6 h (Strv) and fixed, or
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followed by 10% FBS treatment for 15 min (FBS) and fixation. Cells were stained for Sec31, and
imaged by confocal microscopy. Right panel shows graphic representation of the number of
ERES per cell. Results are presented as % of control (Strv) to account for inter-assay variance.
Results are means ± SD from three independent experiments where at least 50 cells were
evaluated per experiment. C, Schematic representation of the results of our mathematical
modelling. In starved cells, Sec16 rarely leave the ERES (thin black dashed arrows). Those that
escaped the ERES will re-bind to and diffuse on ER membranes from where they get captured by
existing ERES (red arrows). In mitogen-treated cells, dissociation is enhanced (thick black
dashed arrows), and rebinding Sec16 molecules can form local assemblies on ER membranes that
attract even more Sec16, thereby growing new ERES. D, HeLa cells were serum starved for 4 h
(Strv) and stimulated with 10% FBS for 15 min (FBS) followed by fixation immunostaining, and
confocal microscopy. Upper panel shows representative images of ERES as well as masks of
counted particles as appearing for analysis. Lower panel shows graphic representation of ERES
size presented as fold change of Strv. This value amounts to 96.9 ± 19.2 pixels/ERES (~9.8x9.8
pixels, or 462x462 nm for each ERES). Results are means ± SD from three independent
experiments with at least 10 cells per experiment. Asterisks indicate statistically significant
differences (*, P < 0.05) as determined by paired Student’s t-test. E, HeLa cells expressing YFPSec31A were serum starved for 4 h. Live imaging was started upon addition of 10% FBS and an
29
image was acquired every 60 seconds. Stills are shown of indicated time points. The right most
parts are magnified areas and number next to them are the average ERES size (in pixels) and their
number within the displayed region. Scale bars in this image are 10µm.
Figure 6. FRAP analysis of Sec16 dynamics. A, HeLa cells expression GFP-tagged wild type
Sec16 (left) or Sec16-Maddin (right). B, HeLa cells expressing wild type GFP-Sec16. Cells were
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serum starved for 6 h. FRAP curves were recorded from the same coverslip before (black
symbols) and after stimulation with 50 ng/ml EGF (+EGF; white symbols). FRAP curves are
shown as mean of three independent experiments where at least 5 curves were recorded in each
experiment. Fluorescence values were normalized to the pre-bleach intensity. Images below the
curves depict representative examples of bleached ERES before (pre-bleach) and after bleaching
at the indicated time points. C, same as in panel B, except that cells were pre-treated with
50 µg/mL cycloheximide for 2 h (CHX) before starting FRAP measurements. D, Similar
experimental setting as in panel B except that cells expressing GFP-Sec16-Maddin were used.
E&F, Similar as in panel B, except that cells were transfected with siRNA to Sar1A (E) or
NME6 (F) 72 h prior to FRAP measurement of wild type GFP-Sec16. G, Bar graph depicts the
relative increase in mobile fractions, q, after treatment with EGF determined as indicated in
“Materials and Methods”. WT_NT and WT_CHX indicate the condition of wild type GFP-Sec16
expressing cells treated with solvent and cycloheximide, respectively. Values are means ±SD
from three independent experiments. H, HeLa cells expressing wild-type GFP-Sec16 (WT), GFPSec16-Maddin (Maddin) or GFP (-) were lysed and Sec16 was immunoprecipitated using GFPtrap beads. The immunoprecipitate was eluted from beads and Sec16 was detected by
immunoblotting. The same membrane was probed for the levels of Sec23A (Sec23) or Sec31.
30
Asterisks indicate statistically significant differences (*, P < 0.05) as determined by ANOVA
with Tukey’s post hoc test. Scale bars in this image are 10µm.
Figure 7. Role of COPII and Sec16 in biogenesis of ERES. A, HeLa cells on glass cover slips
were transfected with plasmid encoding wild type GFP-Sec16 (WT), or GFP-Sec16-Maddin
(Maddin), or GFP-Sec16-T415I (T415I). After 24 h, cells were either fixed directly (NT) or
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treated with 10% FBS for 15 min (+FBS) followed by fixation. The number of Sec16-positive
ERES was counted using ImageJ. Lines indicate the median number of ERES/cell from nontreated cells (black lines) or FBS-treated cells (white lines). B-D, HeLa cells were plated on glass
cover slips. After 24 h, cells were either not treated or treated with cycloheximide (CHX) for two
hours prior to the experiment. Cells were permeabilized and washed extensively to deplete COPII
and Sec16. These cells were either fixed directly after washing (washed) or treated with cytosol
harvested from HeLa cells (+cytosol) for 30 min at room temperature followed by fixation. Sec16
was detected using immunofluorescence. The number of Sec16-positive ERES was counted using
ImageJ and the bar graph in the right panel shows the relative increase in Sec16-positive ERES in
the condition “+cytosol” normalized to the condition “washed”. Values are means ± SD from
four (Sec16) or three (Sec31) independent experiments. Asterisks indicate statistically significant
differences (*, P < 0.05) determined by paired Student’s t-test. Scale bar in this image is 10µm.
Figure 8. Sec16 represents a link between the secretory pathway and cell proliferation. A, Upper
panel: Hits from previous screens (Farhan et al.,2010; Simpson et al.,2012) (Farhan hits and
Pepperkok hits) were also anchored to Sec16 and the networks were merged (right network).
Lower panel: All nodes from the merged network were analyzed for enrichment of cellular
processes (GO-term annotations) using the BiNGO plugin in Cytoscape. Cellular processes that
31
are significantly enriched and that are linked to cell growth and proliferation are displayed. B,
HeLa cells were transfected with control siRNA (siControl) or siRNA to Sec16 (siSec16) or
Sar1A (siSar1A). After 24 h or 48 h, cells were detached and counted using a counting chamber.
Results are presented as fold increase of number of cells plated at time point 0 (t0). C, HeLa cells
were transfected with control siRNA (siControl) or siRNA to Sec16 (siSec16), or treated with 5
μg/ml BFA overnight. Lysates were immunoblotted against Caspase-3. Upper panel shows a
Journal of Cell Science
Accepted manuscript
representative experiment and the lower panel an evaluation of three independent experiments
depicting the percentage of cleaved versus total Caspase-3. D, HeLa cells were transfected with
empty vector (control) or wild type Ras (Ras-WT). 24 hours after transfection, cells were lysed
and immunoblotted against indicated proteins. Panel shows representative blot of three
independent experiments. E, HeLa cells were transfected with control siRNA (siControl) or
siRNA to or Egr1+3 (siEgr1+3). After 24 h, cells were transfected with plasmid encoding GFP
(GFP), wild type GFP-Sec16 (Sec16WT), Sec16-Maddin (Maddin), the phosphorylationdeficient Sec16-T415I and Sar1A. 48 hours after transfection, cells were detached and counted
using a counting chamber. Results are presented as fold increase of number of cells plated at time
point 0 (t0). Asterisks in this figure indicate statistically significant differences (*, P < 0.05) as
determined by ANOVA with Tukey’s post hoc test.
32
Strv
siControl
120
c1
Se
si
si
C
on
tr
6
ol
Figure 1
A SS
kDa
Sec16
250
tubulin
50
siSec16
% of SS
100
80
*
60
40
20
0
B
serum starvation (h)
SS
Strv
SS
siSec16 (#1)
Strv
siSar1A (#1)
180
Sec16
Sec31
160
NT
1
2
3
4
6
140
kDa
120
Sec16
250
150
Sec31
Journal of Cell Science
Strv
siControl
% of NT
Accepted manuscript
SS
100
80
60
40
Vinculin
20
100
0
NT
C
1
FBS stimulation (h)
Strv
0.5
1
2
3
2
3
4
6
serum starvation (h)
4
kDa
210
Sec16
250
Sec16
190
Sec31
Vinculin
100
150
Sec31
% of Strv
170
150
130
110
90
Vinculin
70
100
50
Strv
0.5
1
2
3
CHX
D
NT FBS NT FBS
Sec16
tubulin
FBS stimulation (h)
kDa
250
50
4
Figure 2
A
CHX treatment (h)
+MG132
CHX treatment (h)
NT
1
2
3
NT
4
1
2
3
4
Sec16
kDa
250
150
Vinculin
160
140
Sec16 levels
% of NT
120
80
60
40
20
0
NT
1
2
3
4
NT
1
2
3
4
+MG132
CHX treatment (h)
B
serum starvation (h)
+MG132
serum starvation (h)
NT
1
2
3
4
6
NT
1
2
3
4
Sec16
6
kDa
250
150
Vinculin
400
350
Sec16 levels
% of NT
Journal of Cell Science
Accepted manuscript
100
300
250
200
150
100
50
0
NT 1
2
3
4
6
NT 1
2 3 4
+MG132
serum starvation (h)
6
STAT1
SHC1
EP300
MAPKAPK2
(#
+3
r1
+3
Eg
C
kDa
r1
tr
ol
(#
1)
B
IGF1R
on
EGFR
Eg
A
2)
Figure 3
Egr1
75
Vinculin
100
50
Egr3
SRF
EGR1
Vinculin
100
Accepted manuscript
Sec16 promoter
D
kDa
co
nt
r
Eg ol
r1
+
Eg 3 (#
1
r1
+3 )
(#
2)
C
E
control
Egr1 (#3) Egr3 (#1)
kDa
NT FBS
NT FBS
41.3 ± 19.9
Sec16
NT FBS
kDa
Sec16
250
100
Sec16
250
Vinculin
250
Vinculin
150
Sec31
100
100
Sec31
Journal of Cell Science
control ERK2-KD
F
control
Egr1+3, #1
Egr1+3, #2
Egr1+3, #2
t24 min
control
Egr1+3, #1
t0 min
G
120
8
*
80
*
60
40
20
7
fold increase over t0
Number of ERES
% of control
100
6
5
control
4
Egr1+3, #1
3
Egr1+3, #2
2
1
0
control
Egr1+3, #1
Egr1+3, #2
0
t0
t24
Figure 4
A
120
control
Egr1+3 (#1)
*
80
Sec31
Number of ERES
% of control
100
60
40
20
0
Accepted manuscript
Control
Egr1+3 (#1)
Egr1+3 (#2)
B
2)
(#
+3
50
% of control
kDa
AAT
Journal of Cell Science
140
120
r1
+3
r1
Eg
Eg
C
on
tr
ol
(#
1)
160
Eg
r1
+
Eg 3 (
r1 #1
+3 )
(#
2)
0
Egr1+3 (#1)
Egr1+3 (#2)
Control
Egr1+3 (#1)
Egr1+3 (#2)
120
% of control
Albumin
control
140
tr
ol
C
on
75
60
20
100
kDa
80
40
Vinculin
C
100
100
80
60
40
20
50
Tubulin
0
Egr1+3 (#2)
Phosph.
(rapid)
Number of ERES
Sec16
80
60
40
20
0
Strv
FBS
siControl
Strv
D
Strv
FBS
siSec16 (#1)
Strv
FBS
siSar1A (#1)
FBS
E
original image
Secretion
100
0 min
11.47 pixel
17 ERES
5 min
9.88 pixel
mask
ERES
biogenesis
*
120
19 ERES
1.2
ERES size
(fold of Strv)
Journal of Cell Science
C
Synthesis
(slow)
FBS
siSec16
Accepted manuscript
Translation of
secretory proteins
Growth factor
Strv
siControl
B
% of control (Strv)
Figure 5
A
Size of ERES
*
1
11 min
0.8
7.62 pixel
0.6
0.4
0.2
26 ERES
0
Strv
FBS
YFP-Sec31A
B
C
60
45
30
15
0
-1
45
30
15
0
-1
9
19
29
39
60
45
30
15
0
-1
49
F
30
0
-1
49
siSar1A (#1)
siSar1A (#1) +EGF
15
3
fold change in mobile fraction
after EGF treatment
39
45
9
seconds
G
19
29
seconds
CHX
CHX +EGF
9
75
19
29
seconds
39
49
60
fluorescence
(% of pre-bleach)
(% of pre-bleach)
E 60
Maddin
Maddin +EGF
60
9
fluorescence
(% of pre-bleach)
D
WT
WT +EGF
fluorescence
(% of pre-bleach)
GFP-Sec16-Maddin
fluorescence
(% of pre-bleach)
Journal of Cell Science
Accepted manuscript
fluorescence
Figure 6
A GFP-Sec16-WT
19
29
seconds
39
45
30
siNME6
siNME6 +EGF
15
0
-1
49
9
19
29
39
49
seconds
H
2.5
3% Input
2
-
1.5
*
*
*
*
1
0.5
WT_NT
WT_CHX
Maddin siSar1A,#1 siSar1A,#2 siNME6
-
IP
-
WT Maddin
kDa
GFPSec16
Sec23
0
WT Maddin
IP
250
WT Maddin
kDa
GFPSec16
250
150
75
Sec31
Figure 7
800
A
700
ERES/cell
600
WT, NT
WT, FBS
Maddin, NT
Maddin, FBS
T415I, NT
T415I, FBS
Median, NT
Median, FBS
500
400
300
200
Accepted manuscript
100
0
Maddin
WT
T415I
fluorescence intensity/cell (fold increase)
B
fluorescence intensity
2.5
*
fold increase
Journal of Cell Science
2
*
1.5
NT
CHX
1
0.5
0
D
+cytosol
washed
CHX
Sec16
CHX
+cytosol
NT
washed
NT
C
Sec31
Sec31
Sec16
Figure 8
A
Farhan hits
Pepperkok hits
+
=
List of enriched biological processes related to cell proliferation
corr pval
Genes
42127
regulation of cell
proliferation
3.90E-09
PRKCZ CAV1 CLU NFKBIA FOXO1 TNFRSF5 RPS6KB1 PTEN CTNNB1 CUL2 KRAS GAB2 CTGF PRKCA EGFR
AR VHL SMAD4 TP53 RAF1 RB1 KDR CAPN1 MAPK1 SSTR2 NOTCH1 HIF1A HDAC1 NUP62 NME1 JUN VEGFA
PDGFRA MDM2 HGS NGFR SST
8284
positive regulation of cell
proliferation
3.70E-06
PRKCA EGFR PRKCZ CLU TNFRSF5 RPS6KB1 CTNNB1 KDR CAPN1 MAPK1 NOTCH1 HIF1A KRAS HDAC1
GAB2 CTGF NME1 JUN VEGFA PDGFRA MDM2 NGFR
8285
negative regulation of
cell proliferation
1.39E-04
PRKCA AR CAV1 VHL SMAD4 TP53 RAF1 RB1 PTEN CTNNB1 CUL2 SSTR2 NUP62 NME1 JUN HGS SST
50678
regulation of epithelial
cell proliferation
1.80E-04
EGFR AR NOTCH1 NME1 VEGFA PTEN KDR CTNNB1
82
G1/S transition of mitotic
cell cycle
7.33E-05
EGFR CUL2 PIM1 RPS6KB1 RB1 RHOU CDC25A
45786
negative regulation of
cell cycle
9.30E-04
KHDRBS1 EGFR CUL2 HDAC1 MAPK12 TP53 DGKZ NGFR RB1 CDK5
1.37E-07
KHDRBS1 EGFR ROCK2 PIM1 TP53 RPS6KB1 RB1 CDK7 CDK5 PTEN CDC25A SIRT2 CSNK2A2 CUL2 FBXW7
APP HDAC1 MAPK12 CTGF JUN BUB1B MDM2 DGKZ NGFR
GO-ID
Description
regulation of cell cycle
BFA siControl siSec16
C
kDa
9
75
Caspase-3
8
7
6
5
siControl
37
siSec16 (#1)
4
siSar1A (#1)
% cleaved Caspase-3
3
2
1
0
48
Vinculin
pERK
37
37
total ERK
BFA
siControl
siSec16
*
*
5
4
3
2
1
0
siControl
Sec16-wt
Sec16
250
150
0
GFP
Vinculin
5
Sar1A
150
Egr1
10
GFP
75
15
*
*
6
GFP
kDa
E
Cell number
(Fold of t0)
W
as
R
co
nt
D
T
hours
20
Maddin
24
25
Sec16-T415I
0
ro
l
Journal of Cell Science
fold increase over t0
B
Sec16-wt
Accepted manuscript
51726
siEgr1+3